Crop sac qPCR data

Some notes on this dataset: Genes-of-interest PRL and PRLR were run in duplicate for each crop sample, along with two reference genes, ACTB and rpL4. Gene expression was normalized (dCT) by subtracting the geometric mean of the reference gene duplicates (ACTB and rpL4) from the average Ct of the duplicates for each gene-of-interest.
The more positive the dCT, the lower the gene expression relative to reference genes (within a sample)
Can also think of it as “distance from reference gene”
The second normalization (ddCT) involved subtracting the average normalized expression (dCT) of the control group (here, building) from each normalized sample for that gene. More positive numbers imply lower expression than average, more negative numbers imply higher expression than average.
For this reason, it is easier to interpret the -ddCT. Now, postive numbers on -ddCT imply higher expression than average control group, and negative numbers imply lower expression than average control group, as one would expect
Lowest numbers have lowest relative expression, highest numbers have highest relative expression.

Hypothalamus

Transcriptomic data from RNAseq.

Data distribution

*Note: “Focal data” refers to data from only the five stages we are interested in: bldg, inc d3, inc d9, m.inc d8, and hatch“*
Histograms of log transformed data show
HYP PRL: possible bimodal distribution,
HYP PRLR: relatively normal distributon

Gene expression plots

GLM: Hyp PRL

ANOVA indicates a significant effect of stage, but no effect of sex or any interaction.
According to p-values in glm, inc.d3 and hatch significantly differs from manipulation. (But manip does not significantly differ from hatch).

Residual inspection: Residual plots show some outliers with large Cook’s distances and high influence. Tails of Q-Q plot are not on the line.
(data points: #64, 79, 85)

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev      F   Pr(>F)   
## NULL                        102     65.547                   
## stage      4  11.0749        98     54.472 4.9465 0.001165 **
## sex        1   0.0376        97     54.434 0.0672 0.796069   
## stage:sex  4   2.3786        93     52.056 1.0624 0.379680   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                        102     65.547              
## stage      4  11.0749        98     54.472 0.0005504 ***
## sex        1   0.0376        97     54.434 0.7954959    
## stage:sex  4   2.3786        93     52.056 0.3732991    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## glm(formula = log.exp ~ stage + sex, family = "gaussian", data = hprl)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.68291  -0.54851   0.00206   0.51655   1.90204  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4.91034    0.18306  26.824  < 2e-16 ***
## stageinc.d3   -0.01854    0.23689  -0.078  0.93779    
## stageinc.d9   -0.26708    0.22906  -1.166  0.24648    
## stagem.inc.d8 -0.70343    0.23689  -2.969  0.00376 ** 
## stagehatch     0.28803    0.23689   1.216  0.22699    
## sexmale        0.03822    0.14766   0.259  0.79630    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.5611793)
## 
##     Null deviance: 65.547  on 102  degrees of freedom
## Residual deviance: 54.434  on  97  degrees of freedom
## AIC: 240.61
## 
## Number of Fisher Scoring iterations: 2

## 
## Terms added sequentially (first to last)
## 
##             Chisq Df RobustF     Pr(F)    
## (Intercept)        1                      
## stage              4 22.9147 1.072e-06 ***
## sex                1  1.1492    0.2747    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lmRob(formula = log.exp ~ stage + sex, data = hprl)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.85493 -0.42660  0.04911  0.49179  2.06785 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4.84273    0.23857  20.299   <2e-16 ***
## stageinc.d3    0.06815    0.30930   0.220   0.8261    
## stageinc.d9   -0.15250    0.29751  -0.513   0.6094    
## stagem.inc.d8 -0.82107    0.32053  -2.562   0.0120 *  
## stagehatch     0.52766    0.31725   1.663   0.0995 .  
## sexmale       -0.05998    0.19526  -0.307   0.7594    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6204 on 97 degrees of freedom
## Multiple R-Squared: 0.1929 
## 
## Test for Bias:
##             statistic p-value
## M-estimate      11.65 0.07032
## LS-estimate     10.18 0.11724

GLM: Hyp PRLR

“ANOVA” indicates that there is a significant effect of stage and sex on PRLR expression, but no interaction.
Manipulation day 8 is significantly higher than bldg, day 3, and day 9, but NOT significantly different from hatch.
All other stages not signficantly different from each other.
Significant sex difference, where males > females.

Residual inspection: Outliers with large Cook’s distance and influence (datapoints: #2, #87, #90)

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev       F   Pr(>F)    
## NULL                        102    13.4219                     
## stage      4   1.3276        98    12.0944  3.9856 0.004979 ** 
## sex        1   3.9446        97     8.1497 47.3707 6.76e-10 ***
## stage:sex  4   0.4054        93     7.7443  1.2172 0.308847    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                        102    13.4219              
## stage      4   1.3276        98    12.0944  0.003097 ** 
## sex        1   3.9446        97     8.1497 5.875e-12 ***
## stage:sex  4   0.4054        93     7.7443  0.301015    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model 1: log.exp ~ stage + sex
## Model 2: log.exp ~ stage * sex
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1        97     8.1497                     
## 2        93     7.7443  4  0.40544    0.301
## 
## Call:
## glm(formula = log.exp ~ stage + sex, family = "gaussian", data = hr)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.45932  -0.13168   0.03392   0.17965   0.51591  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4.61608    0.07083  65.171  < 2e-16 ***
## stageinc.d3    0.16021    0.09166   1.748 0.083651 .  
## stageinc.d9    0.25060    0.08863   2.827 0.005700 ** 
## stagem.inc.d8  0.34172    0.09166   3.728 0.000325 ***
## stagehatch     0.25171    0.09166   2.746 0.007188 ** 
## sexmale        0.39148    0.05713   6.852 6.72e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08401782)
## 
##     Null deviance: 13.4219  on 102  degrees of freedom
## Residual deviance:  8.1497  on  97  degrees of freedom
## AIC: 45.017
## 
## Number of Fisher Scoring iterations: 2

## 
## Terms added sequentially (first to last)
## 
##             Chisq Df RobustF     Pr(F)    
## (Intercept)        1                      
## stage              4   9.329  0.001856 ** 
## sex                1  58.272 7.327e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lmRob(formula = log.exp ~ stage + sex, data = hr)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -1.540520 -0.148638 -0.001309  0.152009  0.491595 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4.68893    0.06403  73.231  < 2e-16 ***
## stageinc.d3    0.08201    0.08204   1.000  0.32002    
## stageinc.d9    0.21514    0.08027   2.680  0.00865 ** 
## stagem.inc.d8  0.26469    0.08195   3.230  0.00169 ** 
## stagehatch     0.18613    0.08222   2.264  0.02581 *  
## sexmale        0.39982    0.05106   7.830 6.15e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2411 on 97 degrees of freedom
## Multiple R-Squared: 0.425 
## 
## Test for Bias:
##             statistic p-value
## M-estimate      4.943 0.55109
## LS-estimate    11.951 0.06307

Pituitary

Transcriptomic data from RNAseq.

Data distribution

Histograms of log transformed data show
PIT PRL: possible bimodal distribution, w/ one large outlier,
PIT PRLR: relatively normal distributon
##Gene exp. plots

GLM: Pit PRL

“ANOVA” : significant effects of stage and sex, with no interaction.
Manipulation day 8 is significantly higher than bldg, day 3, and day 9, but NOT significantly different from hatch.
All other stages not signficantly different from each other.
Significant sex difference, where males < females.

Residual inspection: Outliers with large Cook’s distance and influence (datapoints: #8, #28, #78)

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev       F    Pr(>F)    
## NULL                        103    156.292                      
## stage      4   86.123        99     70.169 33.5351 < 2.2e-16 ***
## sex        1    4.733        98     65.437  7.3711  0.007887 ** 
## stage:sex  4    5.085        94     60.351  1.9802  0.103842    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                        103    156.292              
## stage      4   86.123        99     70.169 < 2.2e-16 ***
## sex        1    4.733        98     65.437  0.006628 ** 
## stage:sex  4    5.085        94     60.351  0.094528 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model 1: log.exp ~ stage * sex
## Model 2: log.exp ~ stage + sex
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)  
## 1        94     60.351                       
## 2        98     65.437 -4  -5.0853  0.09453 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Terms added sequentially (first to last)
## 
##             Chisq Df RobustF   Pr(F)    
## (Intercept)        1                    
## stage              4 120.078 < 2e-16 ***
## sex                1   5.553 0.01634 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lmRob(formula = log.exp ~ stage + sex, data = pp)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -6.3856166 -0.2955798  0.0005936  0.2535610  0.9729274 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    10.7578     0.1349  79.746  < 2e-16 ***
## stageinc.d3     0.2188     0.1751   1.250 0.214437    
## stageinc.d9     0.4043     0.1605   2.520 0.013365 *  
## stagem.inc.d8   0.6213     0.1710   3.634 0.000446 ***
## stagehatch      2.3795     0.1685  14.121  < 2e-16 ***
## sexmale        -0.1984     0.1052  -1.887 0.062169 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3825 on 98 degrees of freedom
## Multiple R-Squared: 0.4843 
## 
## Test for Bias:
##             statistic   p-value
## M-estimate      10.49 0.1056113
## LS-estimate     23.65 0.0006067

GLM: Pit PRLR

“ANOVA” : significant effects of sex only Manipulation day 8 is significantly different from hatch and building, but not incubation stages.
Significant sex difference, where males > females.

Residual inspection: Outliers with large Cook’s distance and influence (datapoints: #5, #18, #45)

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev       F    Pr(>F)    
## NULL                        103     22.624                      
## stage      4   1.0198        99     21.604  1.4611    0.2203    
## sex        1   4.7463        98     16.858 27.1989 1.088e-06 ***
## stage:sex  4   0.4545        94     16.403  0.6511    0.6275    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp
## 
## Terms added sequentially (first to last)
## 
## 
##           Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                        103     22.624              
## stage      4   1.0198        99     21.604    0.2111    
## sex        1   4.7463        98     16.858 1.836e-07 ***
## stage:sex  4   0.4545        94     16.403    0.6260    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model 1: log.exp ~ stage + sex
## Model 2: log.exp ~ stage * sex
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1        98     16.858                     
## 2        94     16.403  4   0.4545    0.626

## 
## Terms added sequentially (first to last)
## 
##             Chisq Df RobustF     Pr(F)    
## (Intercept)        1                      
## stage              4   7.319  0.005835 ** 
## sex                1  33.268 4.162e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lmRob(formula = log.exp ~ stage + sex, data = pr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.28686 -0.18085  0.01458  0.23334  1.36285 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4.119529   0.094711  43.496  < 2e-16 ***
## stageinc.d3    0.010657   0.126418   0.084   0.9330    
## stageinc.d9    0.002383   0.118602   0.020   0.9840    
## stagem.inc.d8  0.265442   0.121021   2.193   0.0306 *  
## stagehatch    -0.149367   0.124779  -1.197   0.2342    
## sexmale        0.413647   0.077610   5.330 6.29e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3285 on 98 degrees of freedom
## Multiple R-Squared: 0.2846 
## 
## Test for Bias:
##             statistic p-value
## M-estimate    -0.7538 1.00000
## LS-estimate   11.8019 0.06654

Crop sac

Data distribution

Histograms of log transformed data show
Crop PRL: relatively normal, with ~ 4 low outliers
Crop PRLR: relatively normal distributon

Crop sac gene expression plots

GLM: Crop PRL

“ANOVA” : very weak trend towards an interaction, but not really anything else there. Not really anything going on in downstream analyses… Discuss mostly the fact that PRL gene expression was detectable in the crop.

Residual inspection: Outliers with large Cook’s distance and influence (datapoints: #29, #35, #85)

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp.neg
## 
## Terms added sequentially (first to last)
## 
## 
##       Df Deviance Resid. Df Resid. Dev      F Pr(>F)
## NULL                     60     23.443              
## Stage  4  0.11969        56     23.323 0.0732 0.9900
## Sex    1  0.85064        55     22.473 2.0819 0.1547
## 
## Call:
## glm(formula = log.exp.neg ~ Stage + Sex, family = "gaussian", 
##     data = cp)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9239  -0.2818   0.1484   0.3413   0.9717  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.88335    0.19697   9.562 2.71e-13 ***
## StageInc_D3   -0.11143    0.25184  -0.442    0.660    
## StageInc_D9   -0.04919    0.25276  -0.195    0.846    
## StageManip_D8  0.04144    0.31990   0.130    0.897    
## StageHatch    -0.05025    0.24941  -0.201    0.841    
## Sexm           0.23857    0.16535   1.443    0.155    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.4085906)
## 
##     Null deviance: 23.443  on 60  degrees of freedom
## Residual deviance: 22.472  on 55  degrees of freedom
##   (34 observations deleted due to missingness)
## AIC: 126.2
## 
## Number of Fisher Scoring iterations: 2

## 
## Terms added sequentially (first to last)
## 
##             Chisq Df RobustF  Pr(F)
## (Intercept)        1               
## Stage              4 0.34869 0.5473
## Sex                1 2.14174 0.1359
## 
## Call:
## lmRob(formula = log.exp.neg ~ Stage + Sex, data = cp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.07137 -0.37847  0.08886  0.25004  0.96117 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.88294    0.21216   8.875 3.35e-12 ***
## StageInc_D3    0.03644    0.27701   0.132    0.896    
## StageInc_D9   -0.04935    0.26727  -0.185    0.854    
## StageManip_D8  0.32584    0.36389   0.895    0.374    
## StageHatch    -0.01759    0.26451  -0.066    0.947    
## Sexm           0.23956    0.18115   1.322    0.191    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5897 on 55 degrees of freedom
## Multiple R-Squared: 0.0408 
## 
## Test for Bias:
##             statistic p-value
## M-estimate     -0.418  1.0000
## LS-estimate     3.278  0.7732
## 34 observations deleted due to missingness

PRLR Crop

“ANOVA” : weak trend towards stage Not really anything going on in downstream comparisons. Trend towards hatch being significant (may be driving the weak trend towards an effect of stage)?

Residual inspection: Outliers with large Cook’s distance and influence (datapoints: #17, #62, #83), some bad outliers on this one!

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp.neg
## 
## Terms added sequentially (first to last)
## 
## 
##       Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                     87     27.303           
## Stage  4   2.4873        83     24.816  0.08285 .
## Sex    1   0.0927        82     24.723  0.57924  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: log.exp.neg
## 
## Terms added sequentially (first to last)
## 
## 
##       Df Deviance Resid. Df Resid. Dev      F  Pr(>F)  
## NULL                     87     27.303                 
## Stage  4   2.4873        83     24.816 2.0624 0.09323 .
## Sex    1   0.0927        82     24.723 0.3075 0.58075  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## glm(formula = log.exp.neg ~ Stage + Sex, family = "gaussian", 
##     data = cr)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.3939  -0.0512   0.1041   0.2560   0.9704  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.91299    0.14432  13.255   <2e-16 ***
## StageInc_D3    0.12526    0.18117   0.691   0.4913    
## StageInc_D9    0.05969    0.18949   0.315   0.7535    
## StageManip_D8  0.03419    0.19451   0.176   0.8609    
## StageHatch     0.45468    0.18343   2.479   0.0152 *  
## Sexm          -0.06553    0.11818  -0.554   0.5807    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.3015003)
## 
##     Null deviance: 27.303  on 87  degrees of freedom
## Residual deviance: 24.723  on 82  degrees of freedom
##   (7 observations deleted due to missingness)
## AIC: 152.01
## 
## Number of Fisher Scoring iterations: 2

## 
## Terms added sequentially (first to last)
## 
##             Chisq Df RobustF     Pr(F)    
## (Intercept)        1                      
## Stage              4 25.8420 2.216e-07 ***
## Sex                1  0.4496    0.4944    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lmRob(formula = log.exp.neg ~ Stage + Sex, data = cr)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -3.697737 -0.185814 -0.005942  0.112414  0.847851 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    2.18785    0.05790  37.784  < 2e-16 ***
## StageInc_D3   -0.15323    0.07245  -2.115  0.03747 *  
## StageInc_D9   -0.06610    0.07977  -0.829  0.40974    
## StageManip_D8 -0.14709    0.08144  -1.806  0.07458 .  
## StageHatch     0.23433    0.07456   3.143  0.00233 ** 
## Sexm          -0.03658    0.04866  -0.752  0.45441    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2247 on 82 degrees of freedom
## Multiple R-Squared: 0.182 
## 
## Test for Bias:
##             statistic  p-value
## M-estimate    -0.6679 1.000000
## LS-estimate   19.5664 0.003307
## 7 observations deleted due to missingness